A robust CurvLaneNet-YOLO framework for real-time lane curvature estimation and vehicle detection in intelligent transportation applications

Abstract

Current road safety systems face challenges in detecting curved roads and vehicles under varying lighting and weather conditions, leading to lane departure and collision risks. To address this, we propose CurvLaneNet-YOLO, a deep learning framework based on YOLOv8 that simultaneously detects road curvature and vehicles in lanes. The system integrates a parallel polynomial lane detection head into the YOLOv8 architecture, enabling real-time curvature estimation alongside vehicle detection. This research develops an AI-enabled real-time monitoring system to substantially improve road safety. The system makes use of the enhanced capabilities and features of the YOLOv8 model, which involves data preparation, model training, extensive testing, and data augmentation to guarantee model precision. Main goals are estimation of road curvature in real-time, high-performance processing and better assist for the driver. To assess the research, the Cars sample from KITTI dataset on Kaggle, which includes 7,481 images of 640 × 640 resolution, were used. The research achieves an inference latency of 15.4 ± 0.3 ms/image on the test hardware, with mAP@0.5 = 0.9373 and mAP@0.5:0.95 = 0.9217, indicating significant improvements over existing work. This research demonstrates the feasibility of using deep learning techniques for vehicle detection and road curvature estimation in real-time. The results presented in this study show promising potential to integrate into a driver-assistance system, although the data used here is limited to a proof-of-concept validation on the KITTI dataset. The suggested solution is competitive in terms of accuracy and inference time on embedded hardware, providing a potential roadmap for its application in real-world scenarios within the context of intelligent transportation systems.